The data structure and inferential goals for provider and physician profiling, gene expression studies;most health-related science and policy studies require a hierarchical, random effects model. Valid and efficient estimation of population parameters, variance components, and unit-specific random effects (provider, clinician-, region- or gene-specific latent attributes), all in the context of valid uncertainty assessments. Properly developed Bayesian models effectively accomplish these goals, accounting for variance components and other uncertainties, improving estimation of random effects. Guided by a loss function, the approach structures non-standard inferences such as ranking (including identification of extremely poor and good performers) and estimating the histogram of random unit-specific effects. Inferences must perform well for the target goal(s) and combine efficiency (making good use of the available information) with robustness (high efficiency over a broad range of underlying assumptions). The proposed research builds on recently developed and evaluated ranking/percentiling methods. These studies have identified areas requiring additional research, including new ranking methods to address potential drawbacks of current methods, developing data analytic quantification of a method's operating characteristics, implementation of univariate and multivariate robust priors and histogram estimates. Research will be based on mathematical and simulation-based analysis;analysis of health services and gene expression datasets that illustrate approaches and produce substantive findings. Continued development of R-based, simulation and data analysis software will ensure "reproducible research" and allow others to implement procedures. A pre-doctoral Biostatistics student at Johns Hopkins will be supported and mentored. Proposed research will produce innovative statistical methodology, provide guidance on trade-offs in addressing multiple goals, communicate interesting and informative case studies, educate and acculturate a pre-doctoral student.